We describe a deep learning convolutional neural network
(CNN) for enhancing low resolution multispectral satellite
imagery without the use of a panchromatic image. For training,
low resolution images are used as input and corresponding high
resolution images are used as the target output (label). The CNN
learns to automatically extract hierarchical features that can be
used to enhance low resolution imagery. The trained network
can then be effectively used for super-resolution enhancement of
low resolution multispectral images where no corresponding
high resolution image is available. The CNN enhances all four
spectral bands of the low resolution image simultaneously and
adjusts pixel values of the low resolution to match the dynamic
range of the high resolution image. The CNN yields higher
quality images than standard image resampling methods.